Artículos en Revistas
Permanent URI for this collectionhttp://48.217.138.120/handle/20.500.12272/538
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Item Capillary hydrodynamic fractionation of hydrophobic colloids: errors in the estimated particle size distribution(2013) Vega, Jorge Rubén; Clementi, Luis A.; Arretxe, Zohartze; Aguirreurreta, Ziortza; Agirre, Amaia; Leiza, José R.; Gugliotta, Luis M.Capillary hydrodynamic fractionation (CHDF) with turbidity detection at a single wavelength is an analytical technique that is often used for sizing the submicrometric particles of hydrophobic colloids. This article investigates three sources of errors that affect the particle size distribution (PSD) estimated by CHDF: diameter calibration errors, uncertainties in the particle refractive index (PRI), and instrumental broadening (IB). The study is based on simulated and experimental examples that involve unimodal and bimodal PSDs. Small errors in the diameter calibration curve can produce important deviations in the number average diameter due to systematic shifts suffered by the PSD modes. Moderate uncertainties in the PRI are unimportant in the analysis of unimodal PSDs, but in the specific case of bimodal PSDs, errors in the PRI can strongly affect the estimated number concentration of each mode. The typical IB correction (based on the IB function estimated from narrow standards) produces slightly erroneous average diameters but can lead to PSDs with underestimated widths and distorted shapes. In practice, the three investigated sources of errors can be present simultaneously, and uncertainties in the average diameters, the shape and width of the PSD, and the number concentration of the PSD modes are unavoidable.Item A fault detection and diagnosis technique for multivariate processes using a PLS-decomposition of the measurement space(2013) Vega, Jorge Rubén; Godoy, José Luis; Marchetti, JacintoA newstatisticalmonitoring technique based on partial least squares (PLS) is proposed for fault detection and diagnosis inmultivariate processes that exhibit collinearmeasurements. A typical PLS regression (PLSR)modeling strategy is first extended by adding the projections of the model outputs to the latent space. Then, a PLS decomposition of the measurements into four terms that belongs to four different subspaces is derived. In order to online monitor the PLS-projections in each subspace, new specific statistics with non-overlapped domains are combined into a single index able to detect process anomalies. To reach a complete diagnosis, a further decomposition of each statistic was defined as a sum of variable contributions. By adequately processing all this information, the technique is able to: i) detect an anomaly through a single combined index, ii) diagnose the anomaly class from the observed pattern of the four component statistics with respect to their respective confidence intervals, and iii) identify the disturbed variables based on the analysis of themain variable contributions to each of the four subspaces. The effectiveness observed in the simulated examples suggests the potential application of this technique to real production systems.Item Estimation of the particle size distribution of colloids from multiangle dynamic light scattering measurements with particle swarm optimization(2015) Vega, Jorge Rubén; Clementi, Luis A.In this paper particle Swarm Optimization (PSO) algorithms are applied to estimate the particle size distribution (PSD) of a colloidal system from the average PSD diameters, which are measured by multi-angle dynamic light scattering. The system is considered a nonlinear inverse problem, and for this reason the estimation procedure requires a Tikhonov regularization method. The inverse problem is solved through several PSO strategies. The evaluated PSOs are tested through three simulated examples corresponding to polysty-rene (PS) latexes with different PSDs, and two experimental examples obtained by simply mixing 2 PS standards. In general, the evalu-ation results of the PSOs are excellent; and particularly, the PSO with the Trelea’s parameter set shows a better performance than other implemented PSOs.Item New contributions to non linear process monitoring through kernel partial least squares(2013) Vega, Jorge Rubén; Godoy, José Luis; Marchetti, Jacinto; Zumoffen, DavidThe kernel partial least squares (KPLS) method was originally focused on soft-sensor calibration for predicting online quality attributes. In this work, an analysis of the KPLS-based modeling technique and its application to nonlinear process monitoring are presented. To this effect, the measurement decomposition, the development of new specific statistics acting on non-overlapped domains, and the contribution analysis are addressed for purposes of fault detection, diagnosis, and prediction risk assessment. Some practical insights for synthesizing the models are also given, which are related to an appropriate order selection and the adoption of the kernel function parameter. A proper combination of scaled statistics allows the definition of an efficient detection index for monitoring a nonlinear process. The effectiveness of the proposed methods is confirmed by using simulation examples.Item Randomly-branched polymers by size exclusion chromatography with triple detection : computer simulation study for estimating errors in the distribution of molar mass and branching degree(2013) Vega, Jorge Rubén; Clementi, Luis A.; Meira, GregorioThis article theoretically evaluates the biases introduced into the distributions of molar masses (MMD) and the number of long chain branches per molecule (LCBD), when randomly-branched polymers are analyzed by size exclusion chromatography (SEC) with molar mass-sensitive detectors. The MMD of a polymer with tetrafunctional branch units has been calculated with the Stockmayer equation (1943); and an ideal SEC analysis has been simulated that assumes u-solvent, perfect measurements, and perfect fractionation by hydrodynamic volume except for a minor mixing in the detector cells. In ideal SEC, a negligible bias is introduced into the MMD, with the local dispersity exhibiting a maximum of 1.0035 at the high molar masses. This result is consistent with previous theoretical investigations, but differs qualitatively from experimental observationsofpolymerscontainingshort-andlong-chain branches. When including band broadening in the columns while still assuming perfect measurements, the MMDremainsessentiallyunbiased.Incontrast,poorMMD estimates are obtained when the chromatograms are contaminated with additive noise. Only qualitative estimates of the LCBD are possible, due to theoretical limitations combined with propagation of errors in a highly nonlinear calculation procedure.Item Relationships between PCA and PLS-regression(Revista Chem And Intell Lab Syst, 2014) Vega, Jorge Rubén; Godoy, José Luis; Marchetti, Jacinto L.This work aims at comparing several features of Principal Component Analysis (PCA) and Partial Least Squares Regression (PLSR), as techniques typically utilized for modeling, output prediction, and monitoring of multivariate processes. First, geometric properties of the decomposition induced by PLSR are described in relation to the PCA of the separated input and output data (X-PCA and Y-PCA, respectively). Then, analogies between the models derived with PLSR and YX-PCA (i.e., PCA of the joint input–output variables) are presented; and regarding to process monitoring applications, the specific PLSR and YX-PCA fault detection indices are compared. Numerical examples are used to illustrate the relationships between latent models, output predictive models, and fault detection indices. The three alternative approaches (PLSR, YX-PCA and Y-PCA plus X-PCA) are compared with regard to their use for statistical modeling. In particular, a case study is simulated and the results are used for enhancing the comprehension of the PLSR properties and for evaluating the discriminatory capacity of the fault detection indices based on the PLSR and YX-PCA modeling alternatives. Some recommendations are given in order to choose the more appropriate approach for a specific application: 1) PLSR and YX-PCA have similar capacity for fault detection, but PLSR is recommended for process monitoring because it presents a better diagnosing capability; 2) PLSR is more reliable for output prediction purposes (e.g., for soft sensor development); and 3) YX-PCA is recommended for the analysis of latent patterns imbedded in datasets.